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基于全景图的城市街谷平均辐射温度计算模型研究
杨 柳1, 张腾跃2, 刘 衍3, 李 奇4, 雷宸骁2
1.西安建筑科技大学建筑学院,教授;2.西安建筑科技大学建筑学院,硕士研究生;3.( 通讯作者):西安建筑科技大学建筑学 院,教授,liuyan@xauat.edu.cn;4.华南理工大学建筑学院,博士研究生
摘要:
在评估城市街谷热环境对居民生活质 量的影响时,明确街谷的微气候条件,尤其是 平均辐射温度(Mean Radiant Temperature, MRT)对于改善城市热环境具有重要意义。现 阶段,利用基于鱼眼图的天空视域因子(Sky View Factor, SVF)计算方法较为繁琐且难以实 现大范围街谷MRT的时空分布评估。因此,本 研究旨在通过全景图像技术,提出一种快速大 量计算城市街谷MRT时空分布的新方法,并进 一步考虑城市街谷中树木的影响,以改进传统 模型。本研究首先基于全景图像批量获取城市 街谷中的SVF,并结合城市街谷中的几何特征 和植被视域因子,通过改进的平均辐射温度计 算模型对单点的MRT进行计算,同时采用定点 实测数据对该模型进行精确度验证,并将其应 用于西安市街谷MRT的实际计算中。研究结果 显示,经过模型验证,本研究方法具有较高的精度,相对误差大多数情况下在20%以内,RMSE在2.85~4.66 ℃。同时,模型与实测数据的一致 性较好,能够清晰地反映MRT的变化趋势,R2大于0.74,IA大于0.80。与仅考虑单一不透水面的 计算模型相比,考虑树木植被后的模型精度有明显提升,RMSE从5.15 ℃降至3.87 ℃,模型的R2 由0.72提升至0.74,表明改进模型与观测结果具有更好的一致性。提出的基于全景图的MRT计算 方法不仅提高了评估城市街谷热环境的效率和精度,而且通过考虑树木植被的影响,为城市规划 和绿化管理提供了更加科学的指导。此外,本研究的方法和结论能够为城市热岛效应的缓解和城 市生活环境的改善提供理论依据和技术支持。通过实例分析,研究成功地应用于西安市,展示了 2021年7月14日上午9:00的街谷MRT分布图,为后续的城市热环境评估和改善工作奠定了基础。
关键词:  平均辐射温度;全景图;城市街谷  视域因子;实地观测
DOI:10.13791/j.cnki.hsfwest.20240222
分类号:
基金项目:国家自然科学基金面上项目(5207840)
Investigation on the predictive model of mean radiant temperature in urban street canyonswith panorama images
YANG Liu,ZHANG Tengyue,LIU Yan,LI Qi,LEI Chenxiao
Abstract:
Urbanization has profoundly transformed natural landscapes into man-made environments, notably converting natural vegetation cover into impervious surfaces. This transition significantly impacts the near-surface energy balance and material exchange, leading to the formation of distinct urban climates. The design and layout of urban areas have a substantial influence on local microclimates, which in turn affects the thermal comfort of residents in those regions. The relationship between urban form and thermal comfort is complex and multifaceted. Urban street canyons, as fundamental components of urban morphology, significantly impact both indoor and outdoor microclimates, human thermal comfort, and energy consumption in buildings. Understanding this relationship is crucial for designing and planning sustainable and livable cities. Previous research has often been limited by the scale of urban data samples, typically focusing on concentrated areas such as neighborhoods and parks, with less attention paid to urban street canyons. This study aims to explore the distribution patterns of thermal comfort within urban street canyons, building on existing models for predicting Mean Radiant Temperature (MRT) and leveraging large-scale data acquisition and computational methods. The validation and optimization of the predictive model were conducted in the Xi’an area. When evaluating the effects of urban street canyon thermal environments on the quality of life for city residents, it is essential to accurately assess the microclimatic conditions within these canyons. Of particular importance is the Mean Radiant Temperature (MRT), a critical factor that significantly influences the thermal comfort of urban environments. Traditional methods, such as those employing fisheye lens photographs to calculate the Sky View Factor (SVF), are both laborintensive and impractical for large-scale assessments of MRT’s spatial and temporal distribution within urban street canyons. This paper introduces a novel approach that utilizes panoramic imaging technology to rapidly calculate MRT across extensive urban areas while incorporating the cooling effects of street-level vegetation, offering a substantial improvement over existing models. The methodology outlined in this research leverages panoramic images to derive the SVF, integrating this with the geometric characteristics and vegetation view factors of urban street canyons. This integration enables the computation of MRT at specific points within the canyons using an enhanced radiation transfer model. The model’s accuracy was rigorously validated using fixed-point measurement data, ensuring its reliability for practical application. The proposed methodwas then applied to calculate the MRT within the street canyons of Xi’an, demonstrating the approach’s effectiveness. The findings from this study indicate a high degree of accuracy in the proposed model, with the majority of relative errors falling within 20%. The Root Mean Square Error (RMSE) ranged between 2.85 and 4.66 ℃, showcasing the model’s precision in estimating MRT. Furthermore, the model exhibited excellent agreement with measured data, accurately reflecting MRT trends over time and space, with a coefficient of determination (R2) greater than 0.74 and an Index of Agreement (IA) greater than 0.80. These metrics underscore the model’s capability to reliably predict MRT variations within urban street canyons. A comparative analysis with models that only consider impermeable surfaces further highlighted the superiority of the proposed approach. Incorporating vegetation into the model led to a significant improvement in accuracy, with RMSE decreasing from 5.15 ℃ to 3.87 ℃ and R2 increasing from 0.72 to 0.74. This improvement confirms the model’s enhanced consistency with observed data, illustrating the beneficial impact of including vegetation in urban thermal models. The innovative methodology proposed for calculating MRT using panoramic images marks a significant advancement in the field of urban microclimate assessment. By facilitating rapid and accurate evaluations of thermal conditions in street canyons on a large scale, this approach addresses the limitations of previous models and meets the needs of urban planners and environmental scientists. The inclusion of vegetation effects in the model not only contributes to a more accurate representation of urban thermal environments but also provides valuable insights for urban planning and green space management. This research demonstrates the potential for leveraging technology to enhance our understanding of urban microclimates, offering a new tool for improving thermal comfort and quality of life in urban areas. Moreover, the application of this model to the street canyons of Xi’an and the subsequent generation of an MRT distribution map for July 14, 2021, at 9:00 a.m., exemplifies the practical utility of the proposed method. This case study not only validates the model’s effectiveness but also illustrates its potential to inform urban design and policy decisions aimed at mitigating the urban heat island effect and enhancing urban thermal comfort. In conclusion, the development of a panoramic image-based method for calculating the MRT in urban street canyons represents a significant contribution to the fields of urban climatology and environmental science. By offering a rapid, accurate, and scalable solution that incorporates the cooling effects of vegetation, this research paves the way for more sustainable urban planning practices. The findings underscore the importance of integrating natural elements into urban environments to improve thermal comfort, thereby enhancing the overall well-being of city dwellers.
Key words:  mean radiant temperature  panorama images  urban street canyons  view factors  field research